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A review of predictive analysis techniques of diabetes prevalence

By: Nicholas Musau, Josiah Ochieng Kuja

Key Words: IBM watson analytics, Diabetes, Prevalence.

Int. J. Biomol. & Biomed. 8(1), 1-9, December 2018.

Certification: ijbb 2018 0003 [Generate Certificate]

Abstract

Diabetes is a significant cause of mortality and morbidity in different continents of the world. Many diabetes victims are found in developing countries like Sub-Saharan Africa. However, some developed nations like United States and Europe record significant records on diabetes prevalence. Studies project a dramatic increase of the infection spread in the world. Also, it provides visible results on the effects of the infection among the victims and the society at large. Studies of type 2 diabetes prevalence indicate minimal rates in rural population and moderate results in the developed regions of the same country. Such results create an alarm to the unaffected regions. The frequent observation of modestly high prevalence of impaired glucose tolerance in areas with low prevalence of diabetes indicate risk of early stage of diabetes epidemics.

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A review of predictive analysis techniques of diabetes prevalence

Alotaibi A, Perry L, Gholizadeh L, Al-Ganmi A. 2017. Incidence and prevalence rates of diabetes mellitus in Saudi Arabia: An overview. Journal of epidemiology and global health.

Bharati DR, Pal R, Kar S, Rekha R, Yamuna TV, Basu M. 2011. Prevalence and determinants of diabetes mellitus in Puducherry, South India. Journal of pharmacy & bioallied sciences 34, 513.

Centers for Disease Control and Prevention. 2014. National diabetes statistics report: estimates of diabetes and its burden in the United States, 2014. Atlanta, GA: US Department of Health and Human Services, 2014.

Chong R. 2018. Diabetes and Data Science: Applying Predictive Analytics to Health. Retrieved from https://clockwork-solutions.com/diabetes-data-science

De Fronzo RA, Tripathy D, Schwenke DC, Banerji M, Bray GA, Buchanan TA, Ratner RE. 2013. Prediction of diabetes based on baseline metabolic characteristics in individuals at high risk. Diabetes Care 36-11, 3607-3612.

Hall V, Thomsen RW, Henriksen O, Lohse N. 2011. Diabetes in Sub Saharan Africa 1999-2011: epidemiology and public health implications. A systematic review. BMC public health 11-1, 564.

Harvey JN, Craney L, Kelly D. 2002. Estimation of the prevalence of diagnosed diabetes from primary care and secondary care source data: comparison of record linkage with capture-recapture analysis. Journal of Epidemiology & Community Health 56-1, 18-23.

Hennink MM, Kaiser BN, Sekar S, Griswold EP, Ali MK. 2017. How are qualitative methods used in diabetes research? A 30-year systematic review. Global public health 12-2, 200-219.

Kalwat MA, Wichaidit C, Nava Garcia AY, McCoy MK, McGlynn K, Hwang IH, Cobb MH. 2016. Insulin promoter-driven Gaussia luciferase-based insulin secretion biosensor assay for discovery of β-cell glucose-sensing pathways. ACS sensors 110, 1208-1212.

Liebert, M. 2018. Big data is transforming healthcare – from diabetes to the ER to research. www.eurekalert.org/pub_releases/2016-02/mali-bdi020216.php

Menke A, Casagrande S, Geiss L, Cowie CC. 2015. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. Jama 314-10, 1021-1029.

Merriam PA, Tellez TL, Rosal MC, Olendzki BC, Ma Y, Pagoto SL, Ockene IS. 2009. Methodology of a diabetes prevention translational research project utilizing a community-academic partnership for implementation in an underserved Latino community. BMC medical research methodology 9-1, 20.

Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Howard VJ. 2015. Heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circulation, CIR-0000000000000350.

Murray CJ, Lopez AD. 1997. Mortality by cause for eight regions of the world: Global Burden of Disease Study. The lancet 349-9061, 1269-1276.

Naicker S, Ashuntantang G. 2017. End stage renal disease in Sub-Saharan Africa. In Chronic Kidney Disease in Disadvantaged Populations pp. 125-137.

Rao CR, Kamath VG, Shetty A, Kamath A. 2010. A study on the prevalence of type 2 diabetes in coastal Karnataka. International journal of diabetes in developing countries 30(2), 80.

Razavian N, Blecker S, Schmidt AM, Smith-McLallen A, Nigam S, Sontag D. 2015. Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data 3-4, 277-287.

Sinnott SJ, McHugh S, Whelton H, Layte R, Barron S, Kearney PM. 2017. Estimating the prevalence and incidence of type 2 diabetes using population level pharmacy claims data: a cross-sectional study. BMJ Open Diabetes Research and Care 5-1, e000288.

World Health Organization. 2016. Global report on diabetes. World Health Organization.

Nicholas Musau, Josiah Ochieng Kuja.
A review of predictive analysis techniques of diabetes prevalence.
Int. J. Biomol. & Biomed. 8(1), 1-9, December 2018.
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